Search Results (83)

The adoption of synchronous optimal pulse-width modulation (SOPWM) in two-level voltage-source inverters (2L-VSIs) offers low switching-to-fundamental-frequency ratio (SFR) operation while maintaining reduced current total harmonic distortion (THD). Despite these advantages, the performance of SOPWM is highly sensitive to signal noise in the modulation index and reference voltage angle. To prevent degradation, conventional PI controllers are conservatively tuned with slow dynamic response, which limits overall system performance. Finite control set model predictive control (FCS-MPC) integrated with SOPWM offers a promising solution, combining the fast dynamic response of FCS-MPC with the optimal steady-state performance of SOPWM. Nevertheless, the intricate tuning of weighting factors in FCS-MPC presents a significant challenge, particularly in balancing between enhanced harmonic performance and fast dynamic response. This paper introduces a simplified FCS-MPC approach that eliminates the need for complex weighting factor tuning while retaining the excellent dynamic performance of FCS-MPC and ensuring the low current THD achieved by SOPWM under steady-state conditions. The efficacy of the proposed method is validated through MATLAB/Simulink (R2023b) simulations and experimental results. Full article

Driven by the high penetration of renewable energy sources and power electronic devices, modern power systems have become increasingly complex, intensifying the demand for accurate and intelligent fault detection. This paper analyzes a total of 81 references to explore the integrated application of artificial intelligence (AI) technologies across all stages of fault data processing, modeling, and optimization. The application potential of AI in fault data processing is firstly analyzed in terms of its performance in mitigating class imbalance, extracting feature information, handling data noise and classification. Then, the modeling of fault detection is classified into rule-driven, data-driven and hybrid-driven methods to evaluate their applicability in scenarios such as transmission lines and distribution networks. The accuracy of fault detection models is also investigated by studying the hyperparameter optimization (HPO) methods. The results indicate that the utilization of AI-driven imbalance handling enhances model accuracy by a range of 16.2% to 26.2%, while deep learning-based feature extraction techniques sustain accuracy levels exceeding 98.5% under a signal-to-noise ratio (SNR) of 10 dB. With a 99.96% detection accuracy, hybrid-driven models applied in fault detection perform the best. For the optimization of fault detection models, heuristic algorithms provide 6.92–19.375% improvement over the baseline models. The findings suggest that AI-driven methodologies in data processing demonstrate notable noise resilience and other benefits. For modeling fault detection, data-driven and hybrid-driven models are presently extensively employed for detecting short-circuit faults, predicting transformer gas trends, and identifying faults in complex and uncertain scenarios. Conversely, rule-driven models are better suited for scenarios possessing a comprehensive experience library and are utilized with less frequency. In the optimization of fault detection models, heuristic algorithms occupy a pivotal position, whereas hyperparameter optimization incorporating reinforcement learning (RL) is better suited for real-time fault detection. The discoveries presented in this paper facilitate the seamless integration of AI with fault detection in modern power systems, thereby advancing their intelligent evolution. Full article

In this paper, in order to effectively reduce the electromagnetic noise of automotive electronic water pumps, a Hybrid Random Carrier Space Vector Pulse Width Modulation Hybrid Random Carrier Space Vector Pulse Width Modulation, (HRCSVPWM) technique based on linear congruential generator (LCG) algorithm is proposed to study the suppression effect of current harmonics and acoustic vibration response with an automotive electronic water pump as the research object. Firstly, the HRCSVPWM based technique is proposed on the basis of SVPWM and pulse width modulation strategies. Secondly, the performance of random numbers generated for HRCSVPWM is analyzed, and it is proposed to use an LCG random number generator to generate excellent random numbers combined with a genetic algorithm to quickly determine the optimal values of three random parameters, namely, random number Ri, mixing degree coefficient Ki, and spreading width Ti, which enhances the stochasticity and spatial traversal of random sequences and ensures the effect of the HRSVPWM control method. Finally, simulation analysis is carried out, and a noise experimental platform is built for experimental verification. The results show that using the improved HRCSVPWM control strategy, compared with the SVPWM control strategy, the total harmonic content decreased by close to 21.81%, and the sound pressure level amplitude decreased by an average of approximately 6 dB. Full article

Background: Quantum machine learning (QML) holds significant promise for advancing medical image classification. However, its practical application to large-scale, high-resolution datasets is constrained by the limited number of qubits and the inherent noise in current quantum hardware. Methods: In this study, we propose the Fused Quantum Dual-Backbone Network (FQDN), a novel hybrid architecture that integrates classical convolutional neural networks (CNNs) with quantum circuits. This design is optimized for the noisy intermediate-scale quantum (NISQ), enabling efficient computation despite hardware limitations. We evaluate FQDN on the task of gastrointestinal (GI) disease classification using wireless capsule endoscopy (WCE) images. Results: The proposed model achieves a substantial reduction in parameter complexity, with a 29.04% decrease in total parameters and a 94.44% reduction in trainable parameters, while outperforming its classical counterpart. FQDN achieves an accuracy of 95.80% on the validation set and 95.42% on the test set. Conclusions: These results demonstrate the potential of QML to enhance diagnostic accuracy in medical imaging. Full article

Addressing the insufficient research on the aerodynamic performance of the coupled last stage and exhaust hood structure in compact marine steam turbines under off-design conditions, this paper establishes for the first time a fully three-dimensional coupled model. It systematically analyzes the influence of the last-stage moving blade shrouds and exhaust hood stiffeners on steam flow loss, static pressure recovery, and vibrational excitation. The research methodology includes the following: employing a hybrid structured-unstructured meshing technique, conducting numerical simulations based on the Shear Stress Transport (SST) turbulence model, and utilizing the static pressure recovery coefficient, total pressure loss coefficient, and cross-sectional flow velocity non-uniformity as performance evaluation metrics. The principal findings are as follows: (1) After installing self-locking shrouds on the moving blades, steam flow loss is reduced by 4.7%, and the outlet pressure non-uniformity decreases by 12.3%. (2) Although the addition of cruciform stiffeners in the diffuser section of the exhaust hood enhances structural rigidity, it results in an 8.4% decrease in the static pressure recovery coefficient, necessitating further optimization of geometric parameters. (3) The coupled model exhibits optimal aerodynamic performance at a 50% design flow rate and 100% design exhaust pressure. The results provide a theoretical basis for the structural optimization of low-noise compact steam turbines. Full article

This study investigates a dynamic switching method for signal relay protocols in Cooperative Pattern Division Multiple Access (Co-PDMA) networks. The proposed approach aims to fully utilize the advantages of signal relays in fading-prone environment while simultaneously reducing the network outage probability and improving the throughput and energy efficiency. To demonstrate the necessity of implementing the dynamic switching method for signal relay protocols, Co-PDMA networks with Decode-and-Forward (DF) or Amplify-and-Forward (AF) protocols are explored over Nakagami-m fading. Based on the analysis of these two scenarios, the overall outage probability, throughput, and energy efficiency of the Co-PDMA network with a dynamic DF/AF protocol are determined. The results demonstrate that the proposed method selects the optimal signal relay protocol for forwarding user data in a simple and efficient manner across varying transmit signal-to-noise ratios, quality of service, and signal relay locations. Compared with fixed signal relay protocols, the proposed method is more conducive to achieving green communication in Co-PDMA networks, as it enhances communication reliability and the total volume of data transmitted. Full article

Time-varying lumped disturbance and measurement noise are primary obstacles that restrict the control performance of permanent magnet synchronous motor (PMSM) drives. To tackle these obstacles, an adaptive nonsingular terminal sliding mode (ANTSM) algorithm is combined with augmented recursive sliding mode observer (ARSMO) for PMSM speed regulation system in this paper. Generally, conventional nonsingular terminal sliding mode (NTSM) controller adopts a fixed and conservative control gain to suppress the time-varying disturbance, which will lead to unsatisfactory steady-state performance. Without requiring any information of the time-varying disturbance in advance, a novel barrier function adaptive algorithm is utilized to adjust the gain of NTSM controller online according to the amplitude of disturbance. In addition, the ARSMO is emoloyed to estimate the total disturbance and motor speed simultaneously, thereby alleviating the negative impact of measurement noise and excessive control gain. Comprehensive experimental results verify that the proposed enhanced ANTSM strategy can optimize the dynamic performance of PMSM system without sacrificing its steady-state performance. Full article

Diffusion-weighted magnetic resonance (DWMR) images were acquired using a custom-designed, 3D-printed breast-mimicking phantom. The smoothing factor of the non-local means (NLM) algorithm was then optimized for noise reduction. Phantoms were fabricated using polylactic acid, polyethylene terephthalate, and various concentrations of polyvinylpyrrolidone. DWMR images were obtained across b-values ranging from zero to 2000 s/mm². Based on image contrast, the NLM algorithm was applied to the b = 1000 s/mm² image, testing smoothing factors from 0.001 to 0.150. The NLM algorithm’s performance was quantitatively evaluated using a single DWMR image acquired from this custom phantom. At the optimized smoothing factor, the signal-to-noise ratio (SNR) improved from 96.87 ± 3.42 to 215.81 ± 4.18, and the contrast-to-noise ratio (CNR) from 43.63 ± 2.97 to 131.98 ± 3.56, representing 2.22-fold and 3.02-fold enhancements, respectively. No formal statistical tests were conducted as the analysis was based on a single acquisition. The optimized NLM algorithm also outperformed conventional denoising methods—median, Wiener, and total variation—in both noise suppression and contrast preservation. These findings suggest that the NLM algorithm with optimized parameters is likely to be more effective than existing approaches for enhancing breast DWMR image quality. However, further validation using in vivo patient datasets is essential to confirm its diagnostic utility and clinical generalizability because of the absence of tissue heterogeneity, motion, and physiological noise in the phantom environment. Full article

Remote sensing of moving targets, particularly pedestrians on the road, is crucial for advanced driver assistance systems. However, pedestrian detection using the radar system remains an ongoing challenge due to the radar cross section (RCS) of pedestrians being much smaller than that of the clutter. Existing radar systems and pedestrian detection methods predominantly rely on the single-polarization radar, while research on the fully polarized radar for pedestrian detection is relatively limited. In this paper, the L-band fully polarimetric radar system is developed for pedestrian detection, and based on the full polarized radar echo HH, HV, VH, and VV, a novel clutter suppression method is proposed, which integrates the optimal polarization states of antennas and optimal scattering characteristics of pedestrians. Moreover, the field experiment has been conducted, and the results demonstrate that the signal-to-clutter-plus-noise ratio (SCNR) of the total power signal of full-polarization echoes is higher than that of single-polarization echoes, and the proposed clutter suppression method is able to reduce the non-stationary clutter and the interference signal generated by the multipath effect, thereby improving the SCNR. Furthermore, the OTSU algorithm is employed to detect pedestrian targets using radar data before and after clutter suppression, and the results demonstrate that the proposed method yields superior detection performance. These findings justify the potential of fully polarimetric radar in enhancing pedestrian detection. Full article

Objective: The accurate visualization of root canal systems on periapical radiographs is critical for successful endodontic treatment. This study aimed to evaluate and compare the effectiveness of several image enhancement algorithms—including a novel Total Variation–Contrast-Limited Adaptive Histogram Equalization (TV-CLAHE) technique—in improving the detectability of root canal configurations in mandibular incisors, using cone-beam computed tomography (CBCT) as the gold standard. A null hypothesis was tested, assuming that enhancement methods would not significantly improve root canal detection compared to original radiographs. Method: A retrospective analysis was conducted on 60 periapical radiographs of mandibular incisors, resulting in 420 images after applying seven enhancement techniques: Histogram Equalization (HE), Contrast-Limited Adaptive Histogram Equalization (CLAHE), CLAHE optimized with Pelican Optimization Algorithm (CLAHE-POA), Global CLAHE (G-CLAHE), k-Caputo Fractional Differential Operator (KCFDO), and the proposed TV-CLAHE. Four experienced observers (two radiologists and two dentists) independently assessed root canal visibility. Subjective evaluation was performed using an own scale inspired by a 5-point Likert scale, and the detection accuracy was compared to the CBCT findings. Quantitative metrics including Peak Signal-to-Noise Ratio (PSNR), Signal-to-Noise Ratio (SNR), image entropy, and Structural Similarity Index Measure (SSIM) were calculated to objectively assess image quality. Results: Root canal detection accuracy improved across all enhancement methods, with the proposed TV-CLAHE algorithm achieving the highest performance (93–98% accuracy), closely approaching CBCT-level visualization. G-CLAHE also showed substantial improvement (up to 92%). Statistical analysis confirmed significant inter-method differences (p < 0.001). TV-CLAHE outperformed all other techniques in subjective quality ratings and yielded superior SNR and entropy values. Conclusions: Advanced image enhancement methods, particularly TV-CLAHE, significantly improve root canal visibility in 2D radiographs and offer a practical, low-cost alternative to CBCT in routine dental diagnostics. These findings support the integration of optimized contrast enhancement techniques into endodontic imaging workflows to reduce the risk of missed canals and improve treatment outcomes. Full article

To address the decline in measurement accuracy of magnetometers due to process errors and environmental interference, as well as the insufficient robustness of traditional calibration algorithms under strong interference conditions, this paper proposes an ellipsoid fitting algorithm based on Dynamic Adaptive Elite Particle Swarm Optimization (DAEPSO). The proposed algorithm integrates three enhancement mechanisms: dynamic stratified elite guidance, adaptive inertia weight adjustment, and inferior particle relearning via Lévy flight, aiming to improve convergence speed, solution accuracy, and noise resistance. First, a magnetometer calibration model is established. Second, the DAEPSO algorithm is employed to fit the ellipsoid parameters. Finally, error calibration is performed based on the optimized ellipsoid parameters. Our simulation experiments demonstrate that compared with the traditional Least Squares Method (LSM) the proposed method reduces the standard deviation of the total magnetic field intensity by 54.73%, effectively improving calibration precision in the presence of outliers. Furthermore, when compared to PSO, TSLPSO, MPSO, and AWPSO, the sum of the absolute distances from the simulation data to the fitted ellipsoidal surface decreases by 53.60%, 41.96%, 53.01%, and 27.40%, respectively. The results from 60 independent experiments show that DAEPSO achieves lower median errors and smaller interquartile ranges than comparative algorithms. In summary, the DAEPSO-based ellipsoid fitting algorithm exhibits high fitting accuracy and strong robustness in environments with intense interference noise, providing reliable theoretical support for practical engineering applications. Full article

The acoustic environment of dental clinics plays a critical role in shaping patient experience, staff performance, and overall clinical effectiveness. This comprehensive review, supported by systematic search procedures, investigates how soundscapes in dental settings influence psychological, physiological, and operational outcomes. A total of 60 peer-reviewed studies were analyzed across dental, healthcare, architectural, and environmental psychology disciplines. Findings indicate that mechanical noise from dental instruments, ambient reverberation, and inadequate acoustic zoning contribute significantly to patient anxiety and professional fatigue. The review identifies emerging strategies for acoustic optimization, including biophilic and sustainable design principles, sound-masking systems, and adaptive sound environments informed by artificial intelligence. Special attention is given to the integration of lean management and circular economy practices for sustainable dental architecture. A design checklist and practical framework are proposed for use by dental professionals, architects, and healthcare planners. Although limited by the predominance of observational studies and geographic bias in the existing literature, this review offers a comprehensive, interdisciplinary synthesis. It highlights the need for future clinical trials, real-time acoustic assessments, and participatory co-design methods to enhance acoustic quality in dental settings. Overall, the study positions sound design as a foundational element in creating patient-centered, ecologically responsible dental environments. Full article

Inverse Laplace transforms (ILTs) are fundamental to a wide range of scientific and engineering applications—from diffusion NMR spectroscopy to medical imaging—yet their numerical inversion remains severely ill-posed, particularly in the presence of noise or sparse data. The primary objective of this study is to develop robust and efficient numerical methods that improve the stability and accuracy of ILT reconstructions under challenging conditions. In this work, we introduce a novel family of Kaczmarz-based ILT solvers that embed advanced regularization directly into the iterative projection framework. We propose three algorithmic variants—Tikhonov–Kaczmarz, total variation (TV)–Kaczmarz, and Wasserstein–Kaczmarz—each incorporating a distinct penalty to stabilize solutions and mitigate noise amplification. The Wasserstein–Kaczmarz method, in particular, leverages optimal transport theory to impose geometric priors, yielding enhanced robustness for multi-modal or highly overlapping distributions. We benchmark these methods against established ILT solvers—including CONTIN, maximum entropy (MaxEnt), TRAIn, ITAMeD, and PALMA—using synthetic single- and multi-modal diffusion distributions contaminated with 1% controlled noise. Quantitative evaluation via mean squared error (MSE), Wasserstein distance, total variation, peak signal-to-noise ratio (PSNR), and runtime demonstrates that Wasserstein–Kaczmarz attains an optimal balance of speed (0.53 s per inversion) and accuracy (MSE = $4.7\times {10}^{-8}$), while TRAIn achieves the highest fidelity (MSE = $1.5\times {10}^{-8}$) at a modest computational cost. These results elucidate the inherent trade-offs between computational efficiency and reconstruction precision and establish regularized Kaczmarz solvers as versatile, high-performance tools for ill-posed inverse problems. Full article

Integrated Sensing and Communication (ISAC) has been enhanced to serve as a pivotal enabler for next-generation communication systems. In the context of target area detection, a UAV-SAR (Unmanned Aerial Vehicle–Synthetic Aperture Radar) based ISAC system, which shares both physical infrastructure and spectrum, can enhance the utilization of spectrum and hardware resources. However, existing studies on UAV-SAR-based ISAC systems for target imaging remain limited. In this study, we first established an ISAC mechanism to enable SAR imaging and communication. Then, we analyzed the energy consumption model, which includes both UAV propulsion and ISAC energy consumption. To maximize system energy efficiency, we propose an optimization method based on sequential convex optimization with linear state-space approximation. Furthermore, we propose a plan with general constraints, including the initial and final positions, the signal-to-noise ratio (SNR) constraint for SAR imaging, the data transmission rate constraint, and the total power limitation of the UAV. To achieve maximum energy efficiency, we jointly optimized the UAV’s trajectory, velocity, communication beamforming, sensing beamforming, and power allocation. Numerical results demonstrate that compared to existing benchmarks and PSO algorithms, the proposed method significantly improves the energy efficiency of UAV-SAR-based ISAC systems through optimized trajectory design. Full article

Continuous flight auger piles (CFAPs) are highly versatile and productive deep foundation elements. Known for their execution speed, low noise, and minimal vibration, they are extensively used in Brazil, particularly for urban projects or environmentally sensitive areas. Technologically, they employ a Real-Time Operation System (RTOS) to control the execution energy for each drilled pile. When used effectively, this energy-based monitoring system can provide information that replaces or correlates with other challenging-to-measure variables, accommodating the impact of various exogenous variables on a pile’s execution and performance. Foundation designers often define one or more characteristic lengths for different pile groups, considered representative for each group despite uncertainties and morphological changes along the terrain. Hence, considering an energy-based control, which enables an individual assessment for each pile, is beneficial given soil’s complexity, which can vary significantly even within a small area. By determining the optimal execution energy, individualized stopping criteria for piles can be established, directly influencing costs and productivity and enhancing reliability. The present paper proposes a methodological workflow to automate the necessary calculations for execution energies, correlate them with bearing capacities measured by load tests or estimated from standard soil surveys, and predict the execution energy and corresponding stopping criteria for the drilling depth of each pile. This study presents a case study to illustrate the methodology proposed, accounting for a real construction site with multiple piles. It shows that considering fixed-length piles may not favor safety, as the energy-based analysis revealed that some piles needed longer shafts. This study also shows that for the 316 CFAPs analyzed with depths ranging from 8 to 14 m, a total of 564 m of pile shafts was unnecessary (which accounted for more than 110 m³ of concrete), indicating that cost optimization is possible. Overall, these analyses improve design safety and reliability while reducing execution costs. The results demonstrate that execution energy can serve as a proxy for subsurface resistance, correlating well with NSPT values and bearing capacity estimations. The methodology enables the individualized assessment of pile performance and reveal the potential for improving the reliability and cost-effectiveness of the geotechnical design process. Full article